diff --git a/articles/TQ00-introduction-to-tidyquant.html b/articles/TQ00-introduction-to-tidyquant.html index 0e19e2c5..ded4436c 100644 --- a/articles/TQ00-introduction-to-tidyquant.html +++ b/articles/TQ00-introduction-to-tidyquant.html @@ -101,7 +101,7 @@
Excel Users

Matt Dancho

-

2023-10-01

+

2023-10-03

Source: vignettes/TQ00-introduction-to-tidyquant.Rmd
TQ00-introduction-to-tidyquant.Rmd
@@ -126,8 +126,8 @@

Benefitszoo, xts, quantmod, TTR, and PerformanceAnalytics -
  • Designed for modeling and scaling analyses using the the -tidyverse tools in R +
  • Designed for modeling and scaling analyses using the +tidyverse tools in R for Data Science
  • Implements ggplot2 functionality for beautiful and @@ -222,16 +222,17 @@

    Performance Analysi range of theories and methods for analyzing risk versus reward. The PerformanceAnalytics package consolidates many of the most widely used performance metrics as functions that can be applied to -stock or portfolio returns. tidquant implements the +stock or portfolio returns. tidyquant implements the functionality with two primary functions:

    • -tq_performance implements the performance analysis +tq_performance() implements the performance analysis functions in a tidy way, enabling scaling analysis using the split, apply, combine framework.
    • -tq_portfolio provides a useful toolset for aggregating -a group of individual asset returns into one or many portfolios.
      • +tq_portfolio() provides a useful toolset for +aggregating a group of individual asset returns into one or many +portfolios.

    Performance is based on the statistical properties of returns, and as a result both functions use returns as opposed to stock diff --git a/articles/TQ01-core-functions-in-tidyquant.html b/articles/TQ01-core-functions-in-tidyquant.html index f6b1caf2..39ac428c 100644 --- a/articles/TQ01-core-functions-in-tidyquant.html +++ b/articles/TQ01-core-functions-in-tidyquant.html @@ -101,7 +101,7 @@

    Excel Users

    Matt Dancho

    -

    2023-10-01

    +

    2023-10-03

    Source: vignettes/TQ01-core-functions-in-tidyquant.Rmd
    TQ01-core-functions-in-tidyquant.Rmd
    @@ -115,11 +115,11 @@

    2023-10-01

    Overview

    -

    The tidyquant package has a core functions with -a lot of power. Few functions means less of a learning curve -for the user, which is why there are only a handful of functions the -user needs to learn to perform the vast majority of financial analysis -tasks. The main functions are:

    +

    The tidyquant package has a few core functions +with a lot of power. Few functions means less of a learning +curve for the user, which is why there are only a handful of functions +the user needs to learn to perform the vast majority of financial +analysis tasks. The main functions are:

    @@ -219,7 +219,7 @@

    2.1 Yahoo! Finance
     aapl_prices  <- tq_get("AAPL", get = "stock.prices", from = " 1990-01-01")
 aapl_prices

    -
    ## # A tibble: 8,502 × 8
+
    ## # A tibble: 8,503 × 8
 ##    symbol date        open  high   low close    volume adjusted
 ##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>
 ##  1 AAPL   1990-01-02 0.315 0.335 0.312 0.333 183198400    0.264
@@ -232,7 +232,7 @@ 
    2.1 Yahoo! Finance##  8 AAPL   1990-01-11 0.324 0.324 0.308 0.308 211052800    0.244
 ##  9 AAPL   1990-01-12 0.306 0.310 0.301 0.308 171897600    0.244
 ## 10 AAPL   1990-01-15 0.308 0.319 0.306 0.306 161739200    0.243
-## # ℹ 8,492 more rows
    
+## # ℹ 8,493 more rows

    Yahoo Japan stock prices can be retrieved using a similar call, get = "stock.prices.japan".

    @@ -459,7 +459,7 @@ 
    2.6 BloombergBloomberg
 provides access to arguably the most comprehensive financial data and is
-actively used by most major financial instutions that work with
+actively used by most major financial institutions that work with
 financial data. The Rblpapi package, an R interface to
 Bloomberg, has been integrated into tidyquant as follows.
 The benefit of the integration is the scalability since we can
@@ -653,7 +653,7 @@ 
    Mutate rolling regressions wi
 
      
 
    1. Get returns
      2. 
 
    2. Create a custom function
      3. 
-
    3. Apply the custom function accross a rolling window using
+
    4. Apply the custom function across a rolling window using
 tq_mutate(mutate_fun = rollapply)
 
      5. 
 
    
diff --git a/articles/TQ02-quant-integrations-in-tidyquant.html b/articles/TQ02-quant-integrations-in-tidyquant.html
index 073fea6e..bf751f4e 100644
--- a/articles/TQ02-quant-integrations-in-tidyquant.html
+++ b/articles/TQ02-quant-integrations-in-tidyquant.html
@@ -101,7 +101,7 @@ 
    Excel Users
    
                         
    Matt
 Dancho
    
             
-            
    2023-10-01
    
+            
    2023-10-03
    
       
       Source: vignettes/TQ02-quant-integrations-in-tidyquant.Rmd
       
    TQ02-quant-integrations-in-tidyquant.Rmd
    
@@ -125,7 +125,7 @@ 
    Overviewxts, zoo, quantmod,
 TTR, and PerformanceAnalytics packages. This
 vignette focuses on the following core functions to demonstrate
-how the integratation works with the quantitative finance packages:
    
+how the integration works with the quantitative finance packages:
    +library(tidyverse)

    1.0 Function Compatibility @@ -196,10 +193,10 @@

    xts Functionality## [1] "apply.daily" "apply.monthly" "apply.quarterly" "apply.weekly" ## [5] "apply.yearly" "diff.xts" "lag.xts" "period.apply" ## [9] "period.max" "period.min" "period.prod" "period.sum" -## [13] "periodicity" "to_period" "to.daily" "to.hourly" -## [17] "to.minutes" "to.minutes10" "to.minutes15" "to.minutes3" -## [21] "to.minutes30" "to.minutes5" "to.monthly" "to.period" -## [25] "to.quarterly" "to.weekly" "to.yearly" +## [13] "periodicity" "to.daily" "to.hourly" "to.minutes" +## [17] "to.minutes10" "to.minutes15" "to.minutes3" "to.minutes30" +## [21] "to.minutes5" "to.monthly" "to.period" "to.quarterly" +## [25] "to.weekly" "to.yearly" "to_period"

    The xts functions that are compatible are listed above. Generally speaking, these are the:

      @@ -412,7 +409,7 @@

      TTR FunctionalityExample 1A: Getting and

      Example 1B: Getting Daily Log Returns

      -

      Daily log returns follows a similar approach. Normally I go with a -transmute function, tq_transmute, because the +

      Daily log returns follow a similar approach. Normally I go with a +transmute function, tq_transmute(), because the periodReturn function accepts different periodicity options, and anything other than daily will blow up a mutation. But, in our situation the period returns periodicity is the same as the stock @@ -560,13 +557,13 @@

      Example 1B: Getting Daily Log Retu mutate_fun = periodReturn, period = "daily", type = "log", - col_rename = "monthly.returns")

      + col_rename = "daily.returns")

     FANG_daily_log_returns %>%
-    ggplot(aes(x = monthly.returns, fill = symbol)) +
+    ggplot(aes(x = daily.returns, fill = symbol)) +
     geom_density(alpha = 0.5) +
     labs(title = "FANG: Charting the Daily Log Returns",
-         x = "Monthly Returns", y = "Density") +
+         x = "Daily Returns", y = "Density") +
     theme_tq() +
     scale_fill_tq() + 
     facet_wrap(~ symbol, ncol = 2)
    @@ -585,9 +582,9 @@

    tq_transmute() and pass the open, high, low, close and volume columns via select = open:volume. Looking at the documentation for to.period, we see that it accepts a -period argument that we can set to "weeks". +period argument that we can set to "months". The result is the OHLCV data returned with the dates changed to one day -per week.

    +per month.

     FANG %>%
     group_by(symbol) %>%
@@ -610,7 +607,7 @@ 
    ## 10 META   2013-10-31  47.2  52    46.5  50.2 248809000
 ## # ℹ 182 more rows

    A common usage case is to reduce the number of points to smooth time -series plots. Let’s check out difference between daily and monthly +series plots. Let’s check out the difference between daily and monthly plots.

    Without Periodicity Aggregation @@ -699,7 +696,7 @@

    E ## 7 META 2013-07-31 0.479 0.0373 ## 8 META 2013-08-30 0.122 -0.0104 ## 9 META 2013-09-30 0.217 0.0253 -## 10 META 2013-10-31 -0.000398 0.0503 +## 10 META 2013-10-31 -0.000398 0.0502 ## # ℹ 182 more rows

    The TTR::runCor function can be used to evaluate rolling correlations using the xy pattern. Looking at the documentation @@ -882,7 +879,7 @@

    Example 6 mechanism for similar assets. While we will not go into a pairs trade analysis, we will analyze the relationship between two similar assets as a precursor to a pairs trade. In this example we will analyze two -similar assets, Mastercard (MA) and Visa (V) to show the relationship +similar assets, MasterCard (MA) and Visa (V) to show the relationship via regression.

    Before we analyze a rolling regression, it’s helpful to view the overall trend in returns. To do this, we use tq_get() to @@ -903,7 +900,7 @@

    Example 6 period = "daily", type = "log", col_rename = "returns") %>% - spread(key = symbol, value = returns)

    + spread(key = symbol, value = returns)

    We can visualize the relationship between the returns of the stock pairs like so.

    @@ -914,7 +911,7 @@ 
    Example 6
     labs(title = "Visualizing Returns Relationship of Stock Pairs") +
     theme_tq()

    -

    We can get statistcs on the relationship from the lm +

    We can get statistics on the relationship from the lm function. The model is highly correlated with a p-value of essential zero. The coefficient estimate for V (Coefficient 1) is 0.8134 indicating a positive relationship, meaning as V increases MA also tends @@ -928,12 +925,12 @@

    Example 6 ## ## Residuals: ## Min 1Q Median 3Q Max -## -0.0269576 -0.0039656 0.0002149 0.0039652 0.0289463 +## -0.0269572 -0.0039655 0.0002151 0.0039652 0.0289458 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 0.0001130 0.0003097 0.365 0.715 -## V 0.8133658 0.0226394 35.927 <2e-16 *** +## V 0.8133659 0.0226394 35.927 <2e-16 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## @@ -941,11 +938,10 @@

    Example 6 ## Multiple R-squared: 0.72, Adjusted R-squared: 0.7194 ## F-statistic: 1291 on 1 and 502 DF, p-value: < 2.2e-16

    While this characterizes the overall relationship, it’s missing the -time aspect. Fortunately, we can use the rollapply function -from the zoo package to plot a rolling regression, showing -how the model coefficent varies on a rolling basis over time. We -calculate rolling regressions with tq_mutate() in two -additional steps:

    +time aspect. Fortunately, we can use the zoo::rollapply() +function to plot a rolling regression, showing how the model coefficient +varies on a rolling basis over time. We calculate rolling regressions +with tq_mutate() in two additional steps:

    1. Create a custom function
    2. Apply the function with @@ -1037,7 +1033,7 @@

      quantmod::periodReturn. Next, we use Return.clean to clean outliers from the return data. -The alpha parameter is the percentage of oultiers to be +The alpha parameter is the percentage of outliers to be cleaned. Finally, the excess returns are calculated using a risk-free rate of 3% (divided by 252 for 252 trade days in one year).

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diff --git a/articles/TQ03-scaling-and-modeling-with-tidyquant.html b/articles/TQ03-scaling-and-modeling-with-tidyquant.html
index 83447365..49463e27 100644
--- a/articles/TQ03-scaling-and-modeling-with-tidyquant.html
+++ b/articles/TQ03-scaling-and-modeling-with-tidyquant.html
@@ -101,7 +101,7 @@ 
      Excel Users
      
                         
      Matt
 Dancho
      
             
-            
      2023-10-01
      
+            
      2023-10-03
      
       
       Source: vignettes/TQ03-scaling-and-modeling-with-tidyquant.Rmd
       
      TQ03-scaling-and-modeling-with-tidyquant.Rmd
      
@@ -118,7 +118,7 @@ 
      Overview
 
      The greatest benefit to tidyquant is the ability to
 apply the data science workflow to easily model and scale your financial
-analysis as described in R for
+analysis as described in R for
 Data Science. Scaling is the process of creating an analysis
 for one asset and then extending it to multiple groups. This idea of
 scaling is incredibly useful to financial analysts because typically one
@@ -135,9 +135,10 @@ 
      Overviewfilter, group_by,
 nest/unnest,
 spread/gather, etc

      3. -
    3. Use purrr: mapping functions with map +
    4. Use purrr: mapping functions with +map()
      5. -
    5. Model financial analysis using the data science workflow in R for Data Science +
    6. Model financial analysis using the data science workflow in R for Data Science

      7. We’ll go through some useful techniques for getting and manipulating @@ -148,12 +149,8 @@

      Prerequisites
      -# Loads tidyquant, lubridate, xts, quantmod, TTR, and PerformanceAnalytics
-library(lubridate)
-library(dplyr)
-library(purrr)
-library(ggplot2)
-library(tidyr)
+# Loads tidyquant, xts, quantmod, TTR, and PerformanceAnalytics
+library(tidyverse)
 library(tidyquant)
      @@ -183,7 +180,7 @@

      Method 1: M ## # ℹ 746 more rows

      The output is a single level tibble with all or the stock prices in one tibble. The auto-generated column name is “symbol”, which can be -pre-emptively renamed by giving the vector a name +preemptively renamed by giving the vector a name (e.g. stocks <- c("AAPL", "GOOG", "META")) and then piping to tq_get.

      @@ -237,16 +234,16 @@

      Method 2B: Use index or exchange## # A tibble: 31 × 8 ## symbol company identifier sedol weight sector shares_held local_currency ## <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <chr> -## 1 UNH UNITEDHEALT… 91324P102 2917… 0.0996 - 5477820 USD -## 2 GS GOLDMAN SAC… 38141G104 2407… 0.0635 - 5477820 USD -## 3 MSFT MICROSOFT C… 594918104 2588… 0.0612 - 5477820 USD -## 4 HD HOME DEPOT … 437076102 2434… 0.0592 - 5477820 USD -## 5 CAT CATERPILLAR… 149123101 2180… 0.0539 - 5477820 USD -## 6 AMGN AMGEN INC 031162100 2023… 0.0529 - 5477820 USD -## 7 MCD MCDONALD S … 580135101 2550… 0.0518 - 5477820 USD -## 8 V VISA INC CL… 92826C839 B2PZ… 0.0452 - 5477820 USD -## 9 CRM SALESFORCE … 79466L302 2310… 0.0397 - 5477820 USD -## 10 BA BOEING CO/T… 097023105 2108… 0.0372 - 5477820 USD +## 1 UNH UNITEDHEALT… 91324P102 2917… 0.101 - 5477820 USD +## 2 MSFT MICROSOFT C… 594918104 2588… 0.0632 - 5477820 USD +## 3 GS GOLDMAN SAC… 38141G104 2407… 0.0626 - 5477820 USD +## 4 HD HOME DEPOT … 437076102 2434… 0.0589 - 5477820 USD +## 5 CAT CATERPILLAR… 149123101 2180… 0.0534 - 5477820 USD +## 6 AMGN AMGEN INC 031162100 2023… 0.0523 - 5477820 USD +## 7 MCD MCDONALD S … 580135101 2550… 0.0506 - 5477820 USD +## 8 V VISA INC CL… 92826C839 B2PZ… 0.0454 - 5477820 USD +## 9 CRM SALESFORCE … 79466L302 2310… 0.0400 - 5477820 USD +## 10 BA BOEING CO/T… 097023105 2108… 0.0369 - 5477820 USD ## # ℹ 21 more rows

      …or, get an exchange.

       -
      ## # A tibble: 8,115 × 15
+
      ## # A tibble: 8,118 × 15
 ##    symbol company      identifier sedol weight sector shares_held local_currency
 ##    <chr>  <chr>        <chr>      <chr>  <dbl> <chr>        <dbl> <chr>         
-##  1 UNH    UNITEDHEALT… 91324P102  2917… 0.0996 -          5477820 USD           
-##  2 UNH    UNITEDHEALT… 91324P102  2917… 0.0996 -          5477820 USD           
-##  3 UNH    UNITEDHEALT… 91324P102  2917… 0.0996 -          5477820 USD           
-##  4 UNH    UNITEDHEALT… 91324P102  2917… 0.0996 -          5477820 USD           
-##  5 UNH    UNITEDHEALT… 91324P102  2917… 0.0996 -          5477820 USD           
-##  6 UNH    UNITEDHEALT… 91324P102  2917… 0.0996 -          5477820 USD           
-##  7 UNH    UNITEDHEALT… 91324P102  2917… 0.0996 -          5477820 USD           
-##  8 UNH    UNITEDHEALT… 91324P102  2917… 0.0996 -          5477820 USD           
-##  9 UNH    UNITEDHEALT… 91324P102  2917… 0.0996 -          5477820 USD           
-## 10 UNH    UNITEDHEALT… 91324P102  2917… 0.0996 -          5477820 USD           
-## # ℹ 8,105 more rows
+##  1 UNH    UNITEDHEALT… 91324P102  2917…  0.101 -          5477820 USD           
+##  2 UNH    UNITEDHEALT… 91324P102  2917…  0.101 -          5477820 USD           
+##  3 UNH    UNITEDHEALT… 91324P102  2917…  0.101 -          5477820 USD           
+##  4 UNH    UNITEDHEALT… 91324P102  2917…  0.101 -          5477820 USD           
+##  5 UNH    UNITEDHEALT… 91324P102  2917…  0.101 -          5477820 USD           
+##  6 UNH    UNITEDHEALT… 91324P102  2917…  0.101 -          5477820 USD           
+##  7 UNH    UNITEDHEALT… 91324P102  2917…  0.101 -          5477820 USD           
+##  8 UNH    UNITEDHEALT… 91324P102  2917…  0.101 -          5477820 USD           
+##  9 UNH    UNITEDHEALT… 91324P102  2917…  0.101 -          5477820 USD           
+## 10 UNH    UNITEDHEALT… 91324P102  2917…  0.101 -          5477820 USD           
+## # ℹ 8,108 more rows
 ## # ℹ 7 more variables: date <date>, open <dbl>, high <dbl>, low <dbl>,
 ## #   close <dbl>, volume <dbl>, adjusted <dbl>
      
 
      You can use any applicable “getter” to get data for every
@@ -338,7 +335,7 @@ 
      3.0 Modeling Financial Data using p
 
      Eventually you will want to begin modeling (or more generally
 applying functions) at scale! One of the best features
 of the tidyverse is the ability to map functions to nested
-tibbles using purrr. From the Many Models chapter of “R for Data Science”, we can apply the
+tibbles using purrr. From the Many Models chapter of “R for Data Science”, we can apply the
 same modeling workflow to financial analysis. Using a two step
 workflow:
      
 
        
@@ -461,7 +458,7 @@ 
        Analyze a Single Stock}

      Testing it out on a single stock. We can see that the “term” that contains the direction of the trend (the slope) is “year(date)”. The -interpetation is that as year increases one unit, the annual returns +interpretation is that as year increases one unit, the annual returns decrease by 3%.

       get_model(AAPL)
      @@ -487,21 +484,21 @@

      Scale to Many Stocks## # A tibble: 5 × 8 ## symbol company identifier sedol weight sector shares_held local_currency ## <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <chr> -## 1 TPR TAPESTRY INC 876030107 BF09… 1.81e-4 - 2586531 USD -## 2 FCX FREEPORT MC… 35671D857 2352… 1.48e-3 - 15983335 USD -## 3 HWM HOWMET AERO… 443201108 BKLJ… 5.04e-4 - 4369675 USD -## 4 ATO ATMOS ENERG… 049560105 2315… 4.35e-4 - 1656490 USD -## 5 EMN EASTMAN CHE… 277432100 2298… 2.55e-4 - 1338851 USD +## 1 FRT FEDERAL REA… 313745101 BN7P… 1.80e-4 - 820430 USD +## 2 PXD PIONEER NAT… 723787107 2690… 1.45e-3 - 2613608 USD +## 3 VRSN VERISIGN INC 92343E102 2142… 5.04e-4 - 1005605 USD +## 4 IEX IDEX CORP 45167R104 2456… 4.31e-4 - 841532 USD +## 5 ALLE ALLEGION PLC G0176J109 BFRT… 2.53e-4 - 992871 USD

      We can now apply our analysis function to the stocks using -dplyr::mutate and purrr::map. The +dplyr::mutate() and purrr::map(). The mutate() function adds a column to our tibble, and the -map() function maps our custom get_model +map() function maps our custom get_model function to our tibble of stocks using the symbol column. -The tidyr::unnest function unrolls the nested data frame so -all of the model statistics are accessable in the top data frame level. -The filter, arrange and select -steps just manipulate the data frame to isolate and arrange the data for -our viewing.

      +The tidyr::unnest() function unrolls the nested data frame +so all of the model statistics are accessible in the top data frame +level. The filter, arrange and +select steps just manipulate the data frame to isolate and +arrange the data for our viewing.

       stocks_model_stats <- stocks_tbl %>%
     select(symbol, company) %>%
@@ -509,13 +506,13 @@ 
      Scale to Many Stocks# Nest 
     group_by(symbol, company) %>%
-    nest() %>%
+    nest() %>%
     
     # Apply the get_model() function to the new "nested" data column
-    mutate(model = map(data, get_model)) %>%
+    mutate(model = map(data, get_model)) %>%
     
     # Unnest and collect slope
-    unnest(model) %>%
+    unnest(model) %>%
     filter(term == "year(date)") %>%
     arrange(desc(estimate)) %>%
     select(-term)
@@ -523,13 +520,13 @@ 
      Scale to Many Stocksstocks_model_stats
      ## # A tibble: 5 × 7
 ## # Groups:   symbol, company [5]
-##   symbol company              data      estimate std.error statistic p.value
-##   <chr>  <chr>                <list>       <dbl>     <dbl>     <dbl>   <dbl>
-## 1 ATO    ATMOS ENERGY CORP    <tibble>  0.0291      0.0126    2.32    0.0490
-## 2 EMN    EASTMAN CHEMICAL CO  <tibble>  0.00566     0.0435    0.130   0.900 
-## 3 TPR    TAPESTRY INC         <tibble>  0.000725    0.0382    0.0190  0.985 
-## 4 FCX    FREEPORT MCMORAN INC <tibble> -0.0460      0.0974   -0.472   0.649 
-## 5 HWM    HOWMET AEROSPACE INC <tibble> NA          NA        NA      NA
      +## symbol company data estimate std.error statistic p.value +## <chr> <chr> <list> <dbl> <dbl> <dbl> <dbl> +## 1 IEX IDEX CORP <tibble> 0.0178 0.0264 0.673 0.520 +## 2 FRT FEDERAL REALTY INVS TRUST <tibble> 0.0170 0.0165 1.03 0.334 +## 3 ALLE ALLEGION PLC <tibble> 0.0157 0.0850 0.185 0.870 +## 4 VRSN VERISIGN INC <tibble> 0.00669 0.0411 0.163 0.875 +## 5 PXD PIONEER NATURAL RESOURCE… <tibble> 0.00664 0.0686 0.0969 0.925

      We’re done! We now have the coefficient of the linear regression that tracks the direction of the trend line. We can easily extend this type of analysis to larger lists or stock indexes. For example, the entire @@ -566,7 +563,7 @@

      Pros and Cons to Built-In Erro

    7. Pros: Long running scripts are not interrupted because of one error

    8. Cons: Errors can be inadvertently handled or -flow downstream if the users does not read the warnings

      9. +flow downstream if the user does not read the warnings

      @@ -588,7 +585,7 @@

      Bad Apples Fail Gracefully, tq_get## ! x = 'BAD APPLE', get = 'stock.prices': Error in getSymbols.yahoo(Symbols = "BAD APPLE", env = <environment>, : Unable to import "BAD APPLE". ## URL using bad/illegal format or missing URL ## Removing BAD APPLE. -
      ## # A tibble: 5,410 × 8
+
      ## # A tibble: 5,412 × 8
 ##    symbol date        open  high   low close    volume adjusted
 ##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>
 ##  1 AAPL   2013-01-02  19.8  19.8  19.3  19.6 560518000     16.8
@@ -601,7 +598,7 @@ 
      Bad Apples Fail Gracefully, tq_get##  8 AAPL   2013-01-11  18.6  18.8  18.5  18.6 350506800     15.9
 ##  9 AAPL   2013-01-14  18.0  18.1  17.8  17.9 734207600     15.3
 ## 10 AAPL   2013-01-15  17.8  17.8  17.3  17.4 876772400     14.9
-## # ℹ 5,400 more rows
      
+## # ℹ 5,402 more rows

      Now switching complete_cases = FALSE will retain any errors as NA values in a nested data frame. Notice that the error message and output change. The error message now states that the @@ -615,7 +612,7 @@

      Bad Apples Fail Gracefully, tq_get## Caused by warning: ## ! x = 'BAD APPLE', get = 'stock.prices': Error in getSymbols.yahoo(Symbols = "BAD APPLE", env = <environment>, : Unable to import "BAD APPLE". ## URL using bad/illegal format or missing URL -
      ## # A tibble: 5,411 × 8
+
      ## # A tibble: 5,413 × 8
 ##    symbol date        open  high   low close    volume adjusted
 ##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>
 ##  1 AAPL   2013-01-02  19.8  19.8  19.3  19.6 560518000     16.8
@@ -628,15 +625,14 @@ 
      Bad Apples Fail Gracefully, tq_get##  8 AAPL   2013-01-11  18.6  18.8  18.5  18.6 350506800     15.9
 ##  9 AAPL   2013-01-14  18.0  18.1  17.8  17.9 734207600     15.3
 ## 10 AAPL   2013-01-15  17.8  17.8  17.3  17.4 876772400     14.9
-## # ℹ 5,401 more rows
      
+## # ℹ 5,403 more rows

      In both cases, the prudent user will review the warnings to determine what happened and whether or not this is acceptable. In the complete_cases = FALSE example, if the user attempts to perform downstream computations at scale, the computations will likely -fail grinding the analysis to a hault. But, the advantage is that the -user will more easily be able to filter to the problem childs to -determine what happened and decide whether this is acceptable or -not.

      +fail grinding the analysis to a halt. But, the advantage is that the +user will more easily be able to filter to the problem root to determine +what happened and decide whether this is acceptable or not.